A photomontage is a pictorial composite made by joining several pictorial elements together. The manipulation of pictures or photographs has a long history that dates back to the invention of photography in the mid 19th century and was fully developed as an art form after World War I. Replacing one or more parts of a host picture with fragment(s) from the same host image or other pictures of such a photomontage may be considered as image tampering. In particular, image tampering is referred to as a malicious manipulation of an image for some purpose. One example is to forge a scene that actually never happened in order to purposely mislead observers of the image.
The development of digital imaging technology have made image tampering easier than ever before. Thus, especially for forensic purposes, there is an increasingly urgent need to detect whether or not an image tampering has occurred.
Image splicing is a simple and commonly used image tampering scheme for the malicious manipulation of images to forge a scene that actually never existed in order to mislead an observer. In particular, image splicing is the process of combining image fragments from the same or different images without further post-processing such as smoothing of boundaries among different fragments. Even without the post-processing, the artifacts introduced by the image splicing may be almost imperceptible. That is, in image splicing situations, it is often hard, if not impossible, for human observers to perceive that any image tampering has occurred. Thus, use of automated or blind detection schemes would be desirable for the task of splicing detection in order to automatically discriminate spliced images from non-spliced (authentic) images.
However, the blind detection of image splicing is a challenging task. Researchers have made several efforts to develop such splicing detection schemes. One example of the background art in this area is a report by H. Farid entitled: “Detection of Digital Forgeries using bispectral analysis,” in Technical Report, AIM-1657, MIT AI Memo, 1999. In Farid, speech signal splicing was considered as a highly non-linear process and higher order spectral analysis, specifically bicoherence, was introduced in the detection task in order to deal with the problem.
A further extension of the above background art example to the image splicing/tampering detection problem is a method of blind splicing detection in a report by T.-T. Ng, S.-F. Chang, and Q. Sun entitled: “Blind detection of photomontage using higher order statistics,” IEEE International Symposium on Circuits and Systems 2004, Vancouver, BC, Canada, May, 2004. However, the reported detection results of Ng et al. of a 72% success rate for image splicing/tampering detection over the Columbia Image Splicing Detection Evaluation Dataset is not high enough for reliable automated detection applications.
From the discussion above, it is clear that image splicing detection is of fundamental importance in the art. The blind image splicing detection methods of the background art have only achieved a probability of successful detection rate of 72% on the standard Columbia Image Splicing Detection Evaluation Dataset. Thus, there is a need in the art for further improvement in image splicing/tampering detection performance with blind methods for authenticating and detecting tampering in images.